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Tailoring a molecule’s properties to improve its binding, specificity, half-life, therapeutic index etc, will greatly improve its chance of success in the clinic. Similarly, assessing and predicting a molecule’s developability will increase its candidacy for advancement into the clinic. Join us at this year’s Optimisation & Developability conference to hear how new approaches such as machine learning, in silico and high throughput approaches are helping to improve candidate selection and lead optimisation.

Sunday, 13 November

Registration Open12:00

Recommended Short Course*14:00

SC1: Developability of Bispecific Antibodies
*Separate registration required. See short courses page for details.

Monday, 14 November

Registration and Morning Coffee (Garden Room)07:30




Chairperson's Opening Remarks

Andreas Evers, PhD, Principal Scientist, Computational Chemistry & Biology, Global Research & Development Discovery Technology, Merck Healthcare KGaA


Tailoring of FcRn-Targeted Molecules for Favorable Binding and Transport Properties

Jan Terje Andersen, Professor, Department of Pharmacology, University of Oslo; Research Group Leader, Department of Immunology, Oslo University Hospital

The half-life of IgG and albumin is roughly 3 weeks on average. This has made IgG the natural choice for design of antibody therapeutics, while albumin is increasingly used as a fusion partner. Remarkably, the half-life of these unrelated proteins is prolonged by a common receptor, FcRn. I will discuss how in-depth insights into FcRn biology guide design of antibody and albumin-based technologies for both invasive and non-invasive delivery.


Increased Antibody Cross-Reactivity by Dual-Affinity Optimization between SARS-CoV Clades

Traian Sulea, PhD, Principal Research Officer, Human Health Therapeutics Research Centre, National Research Council Canada

We applied structure-based constrained optimizations to affinity-mature VHH-72 for the SARS-CoV-2 spike protein while retaining original affinity for SARS-CoV-1. ADAPT-designed mutants improved spike binding and virus neutralization for circulating SARS-CoV-2 variants relative to the parental VHH72-Fc. Remarkably, designed mutants restored binding to the Omicron variant, which was lost in the parental antibody due to spike-RBD mutations. Dual-affinity optimization against sarbecoviruses from different clades can broaden specificity within a given clade.


Development Strategy Considerations for New Modalities

Achim Frauenschuh, Associate Director, Science and Technology, Novartis Pharma AG

In order to meet patient-centralized needs, the diversity of protein therapeutics has increased considerably. New formats drive the evolution of the clinical and regulatory landscapes. In this presentation we explore early-phase development strategies, covering both fundamentals (platforms) and specialized considerations (novel formats). Topics include: Established/successful strategies; Drivers and typical challenges for new strategies; Desirable expertise/capabilities; Transforming risk perceptions into innovation opportunities.


Developability Assessment of Early Stage Biotherapeutics – A Production Perspective

Stefan Kaden, PhD, Senior Scientist, Protein Sciences, MorphoSys AG

Developability assessment of biotherapeutics is a powerful risk mitigation tool. We consider assessment of candidate Manufacturability as a part of our Developability process. Manufacturability assessments of potential lead molecules starts with early productions and observations in analytical biophysical assays adding a filter to rate the ideal candidates. Here we give insights into our Manufacturability process and comment on applied methods generating potential lead molecules for clinical development.


The Discovery, Engineering, and Characterisation of a Highly Potent Anti-Human IL-22 Antibody Using a High-Throughput Single B Cell Platform

John-Paul Silva, PhD, Director Antibody Discovery, UCB Pharma

We describe the application of a high-throughput B cell culture screening approach which combines automation and the isolation of single, antigen-specific IgG-secreting cells. Efficient mining of the antibody immune repertoire using this platform enables the identification of rare antibodies with desirable characteristics. We show how the technology was used in the discovery of a highly potent antibody to human interleukin (IL)-22, a cytokine with multiple functions in inflammatory and tissue responses.

Coffee Break in the Exhibit Hall with Poster Viewing (Verdi and Vivaldi 1&2)10:30



Machine Learning for Multi-Parameter Parallelized Protein Engineering and Accelerated Drug Optimization

Nathan Higginson-Scott, PhD, CTO, Seismic Therapeutic

Machine learning has emerged as a powerful tool for parallelized protein mutation effect prediction. Seismic Therapeutic is shifting how drugs for immunology are discovered and developed by integrating machine learning techniques with structural biology, protein engineering, and translational immunology. We’ll discuss how machine learning can be used to optimize for several critical drug properties simultaneously, thereby reducing the number of iterations needed to craft a molecule suitable for the clinic.


Towards ML-Based Multi-Specific Antibody Optimization

Katrin Reichel, PhD, Senior Data Scientist, Computational & HT Protein Engineering, R&D Biologics Research/Protein Therapeutics, Sanofi

Next-generation multi-specific antibody therapeutics combine functional activities of several mono-specific antibodies and have emerged as successful treatment options for complex, multifactorial illnesses including cancer and inflammatory diseases. Here we report on combining experimental observations with novel in silico approaches to predict correct pairing of light chains with their heavy chain partners to optimize bispecific antibodies in the Y-shaped format.

12:15 Lead Discovery through Optimization: A Case Study in Rapid Antibody Discovery

Jean-Philippe Burckert, PhD, Director Bioinformatics, Charles River

Here we present case studies on three targets: CD47, TFR1 and SIRPa. All leveraging the Distributed Bio's antibody discovery platforms : ​ANTIBODY DISCOVERY: Performance of  our new flagship – the Cosmic™ antibody library, ANTIBODY SCREENING: Implementations of rapid, multi-dimensional on- and off-target screening for thousands of antibody candidates and, ANTIBODY OPTIMIZATION: Affinity and specificity tuning through our Tumbler™ multiparameter optimization platform 

Session Break12:45

12:55 Minimizing Lead Optimization with Data-Driven Antibody Discovery

Néstor Vázquez Bernat, PhD, Application Science Team Lead, Application Science, ENPICOM

During this presentation, the speaker will focus on showing the IGX platform, IGX-Annotate and its integration. 

13:25 The Critical Role of Developability in Drug Development

Campbell Bunce, PhD, Chief Scientific Officer, Abzena

Key learnings from this presentation include: what we mean by developability and where it is optimally applied to drug development, the value of a holistic approach to developability and breakdown of the key developability parameters. 

Session Break13:55



Chairperson's Remarks

Traian Sulea, PhD, Principal Research Officer, Human Health Therapeutics Research Centre, National Research Council Canada


Developability Profiling in Lead Discovery: Keeping the Funnel Wider for Longer

Emma R. Harding, PhD, Director & Head, Molecular Design & Engineering, GlaxoSmithKline

This presentation covers an overview of GSK Discovery's Biopharm developability capability, which builds throughout the discovery process. This includes the ability to generate large panels of leads in a high-throughput, automated manner, enabling parallel biological and developability screening. The resultant multi-faceted lead selection process also highlights risk areas for downstream CMC and in vivo groups, such that de-risking packages can be moved off critical path, accelerating the drug discovery process. 


In silico Approaches towards Developability Assessment and Optimization of NBEs

Andreas Evers, PhD, Principal Scientist, Computational Chemistry & Biology, Global Research & Development Discovery Technology, Merck Healthcare KGaA

We have implemented a pipeline for ML model generation and property prediction for antibodies/VHHs to evaluate sequences and 3D models with regard to their diversity and developability properties, such as liabilities, PTMs, immunogenicity risks, PK properties, and compatibilities with formulations. This pipeline does not only allow to select sequences from high-throughput screening approaches but is also utilized for optimization towards the desired overall developability profile.


Bispecific Antibody-Based Molecules Show Multiple Faces: Developability Challenges and Multi-Dimensional Optimization

Guy J. Georges, PhD, Expert Scientist, Computational Engineering and Data Science, Roche Innovation Center, Munich

1+1 does not equal 2. In this presentation, we will summarize the progress made to assess the developability of our antibody-based future drugs. Biophysical properties are predicted by analyzing sequences, computing parameters on structural models, and performing molecular dynamics. However, while progressing in analyzing single binders, the behavior of bi- or tri-specific formats in different flavors remains challenging. Shape, size, and linkers have their importance.

15:50 Go Beyond Titer and Select Top Producers with Favorable Quality Attributes within 5 days of Cloning

Dagmar Zunner, PhD, Technical Sales Specialist, Berkeley Lights, Inc.

Despite a growing need for earlier information on quality and manufacturability, initial clone screening in mammalian cell line development (CLD) continues to focus on selection for growth and titer. Yet the fastest growing and highest producing clones may not secrete a product with the appropriate quality attributes. Come join us and we will show you how to go beyond titer by selecting manufacturable cell lines as early as possible.

Refreshment Break in the Hall with Poster Viewing (Verdi and Vivaldi 1&2)16:20


Exploring in silico Approaches for Antibody Drug Conjugates Developability Assessments

Marc Bailly, PhD, Principal Scientist, MSD

ADCs are complex engineered therapeutics that combine the precision of the antibody with the activity of the payload, thus lowering the risks of exposure of normal tissues to the potent agents and broadening the potential therapeutic windows compared to traditional chemotherapies. The pace of ADC development is accelerating with the number of investigational agents in human trials growing rapidly. Here we will discuss the developability of ADCs and our approach to speeding up the development of this therapeutic modality which is key to first-in-human and delivering new and better drugs to patients faster.


ON203: A Novel Bioengineered Anti-oxMIF Antibody with Improved Biophysicochemical Properties and Antitumorigenic Activity

Gregor Rossmueller, Graduate Student, OncoOne R&D GmbH

ON203 is an antibody targeting the oxidized form of macrophage migration inhibitory factor (oxMIF). ON203 is the result of an extensive screening process of mutations in the VH and VL domains of imalumab to improve physicochemical properties such as surface-hydrophobicity and aggregation propensity, while maintaining affinity & specificity. The in vitro efficacy and safety were shown by enhanced ADCC and reduced non-specific cytokine release. This translated into improved tumor suppression in vivo.

Welcome Reception in the Exhibit Hall with Poster Viewing (Verdi and Vivaldi 1&2)18:05

Close of Optimisation & Developability Conference19:05